Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x10b920f28>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x10eb4ba58>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
/Users/mtx/anaconda/envs/dlnd-tf-lab/lib/python3.5/site-packages/ipykernel/__main__.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs_real = tf.placeholder(tf.float32, (None, image_width,image_height,image_channels), name='input_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')

    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def leaky_relu(input, alpha = 0.15):
    activation = tf.maximum(alpha * input, input) 
    return activation
In [7]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = 0.2
    
    with tf.variable_scope('discriminator', reuse=reuse):
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = leaky_relu(x1)
        #relu1 = tf.maximum(alpha * x1, x1)
        # 14x14x64
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = leaky_relu(bn2)
        #relu2 = tf.maximum(alpha * bn2, bn2)
        # 7x7x128
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = leaky_relu(bn3)
        #relu3 = tf.maximum(alpha * bn3, bn3)
        # 4x4x256

        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [8]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    #alpha = 0.2
    reuse = False if is_train else True
    
    with tf.variable_scope('generator',reuse=reuse):
        # First fully connected layer
        x1 = tf.layers.dense(z, 4*4*512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 4, 4, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = leaky_relu(x1)
        #x1 = tf.maximum(alpha * x1, x1)
        # 4x4x512 now
        
        x12 = tf.layers.conv2d_transpose(x1,256,kernel_size=4,strides=1,padding='valid')#,use_bias=False)
        x12 = tf.layers.batch_normalization(x12, training=is_train)
        x12 = leaky_relu(x12)
        # 7x7x256 now
        
        x2 = tf.layers.conv2d_transpose(x12,128,kernel_size=5,strides=1,padding='same')#,use_bias=False)
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = leaky_relu(x2)
        # 7x7x128 now
        
        x3 = tf.layers.conv2d_transpose(x2, 64, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = leaky_relu(x3)
        #x3 = tf.maximum(alpha * x3, x3)
        # 14x14x64 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
        # 28x28x3 now
        
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    #alpha = 0.2
    
    g_model = generator(input_z, out_channel_dim)#, alpha=alpha)
    d_model_real, d_logits_real = discriminator(input_real)#, alpha=alpha)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)#, alpha=alpha)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*0.9))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss
    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [12]:
import matplotlib.pyplot as plt
def loss_plots(losses):
    fig, ax = plt.subplots()
    losses = np.array(losses)
    plt.plot(losses.T[0], label='Discriminator', alpha=0.5)
    plt.plot(losses.T[1], label='Generator', alpha=0.5)
    plt.title("Training Losses")
    plt.legend()
    
In [13]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    print_every = 10 
    show_every = 100
    #figsize=(5,5)
    
    if data_image_mode == "RGB":
        out_dim = 3
    elif data_image_mode == "L":
        out_dim = 1
    
    #tf.reset_default_graph()
    #print('data_shape',data_shape)
    #print(data_shape[1],data_shape[2],data_shape[3])
    
    input_real, input_z,l_rate = model_inputs(data_shape[1],data_shape[2],out_dim, z_dim)
    #print(input_real,input_z)
    d_loss, g_loss = model_loss(input_real, input_z, out_dim)
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    saver = tf.train.Saver()
    #n_images = 72
    sample_z = np.random.uniform(-1, 1, size=(show_n_images, z_dim))

    samples, losses = [], []
    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                #print(steps,type(steps),print_every,type(print_every))
                batch_images = batch_images * 2

                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z,l_rate: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images,l_rate: learning_rate})

                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}..".format(epoch_i+1, epoch_count),
                          "Steps {}..".format(steps),
                          "Discriminator Loss: {:.4f}..".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))

                
                if steps % show_every == 0:
                    gen_samples = sess.run(
                                   generator(input_z, out_dim, is_train=False),
                                   feed_dict={input_z: sample_z})
                    samples.append(gen_samples)
                    show_generator_output(sess, show_n_images, input_z, out_dim, data_image_mode)

        saver.save(sess, './checkpoints/generator.ckpt')

    import pickle as pkl
    with open('samples.pkl', 'wb') as f:
        pkl.dump(samples, f)
        
    loss_plots(losses)
    
    return losses, samples
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [14]:
batch_size = 32
z_dim = 100
learning_rate = 0.0008
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2.. Steps 10.. Discriminator Loss: 3.8607.. Generator Loss: 0.0325
Epoch 1/2.. Steps 20.. Discriminator Loss: 0.5745.. Generator Loss: 11.1620
Epoch 1/2.. Steps 30.. Discriminator Loss: 0.9874.. Generator Loss: 4.2572
Epoch 1/2.. Steps 40.. Discriminator Loss: 0.4490.. Generator Loss: 4.4552
Epoch 1/2.. Steps 50.. Discriminator Loss: 1.1882.. Generator Loss: 5.6330
Epoch 1/2.. Steps 60.. Discriminator Loss: 0.8964.. Generator Loss: 1.1762
Epoch 1/2.. Steps 70.. Discriminator Loss: 0.7590.. Generator Loss: 3.7578
Epoch 1/2.. Steps 80.. Discriminator Loss: 0.9911.. Generator Loss: 0.8942
Epoch 1/2.. Steps 90.. Discriminator Loss: 0.4793.. Generator Loss: 2.9299
Epoch 1/2.. Steps 100.. Discriminator Loss: 0.5159.. Generator Loss: 3.1329
Epoch 1/2.. Steps 110.. Discriminator Loss: 0.6063.. Generator Loss: 1.9161
Epoch 1/2.. Steps 120.. Discriminator Loss: 1.7087.. Generator Loss: 0.9728
Epoch 1/2.. Steps 130.. Discriminator Loss: 1.0117.. Generator Loss: 0.9500
Epoch 1/2.. Steps 140.. Discriminator Loss: 1.0823.. Generator Loss: 0.8417
Epoch 1/2.. Steps 150.. Discriminator Loss: 0.8858.. Generator Loss: 1.0275
Epoch 1/2.. Steps 160.. Discriminator Loss: 0.6429.. Generator Loss: 2.1173
Epoch 1/2.. Steps 170.. Discriminator Loss: 1.1014.. Generator Loss: 0.9809
Epoch 1/2.. Steps 180.. Discriminator Loss: 0.6984.. Generator Loss: 1.5932
Epoch 1/2.. Steps 190.. Discriminator Loss: 2.3480.. Generator Loss: 0.2761
Epoch 1/2.. Steps 200.. Discriminator Loss: 1.0669.. Generator Loss: 1.5225
Epoch 1/2.. Steps 210.. Discriminator Loss: 0.9389.. Generator Loss: 3.0042
Epoch 1/2.. Steps 220.. Discriminator Loss: 1.0603.. Generator Loss: 0.7776
Epoch 1/2.. Steps 230.. Discriminator Loss: 0.7714.. Generator Loss: 1.4444
Epoch 1/2.. Steps 240.. Discriminator Loss: 1.3372.. Generator Loss: 0.6538
Epoch 1/2.. Steps 250.. Discriminator Loss: 0.7615.. Generator Loss: 1.6708
Epoch 1/2.. Steps 260.. Discriminator Loss: 0.7176.. Generator Loss: 1.8907
Epoch 1/2.. Steps 270.. Discriminator Loss: 0.9137.. Generator Loss: 1.9132
Epoch 1/2.. Steps 280.. Discriminator Loss: 1.0195.. Generator Loss: 1.9285
Epoch 1/2.. Steps 290.. Discriminator Loss: 2.6817.. Generator Loss: 4.1620
Epoch 1/2.. Steps 300.. Discriminator Loss: 1.0529.. Generator Loss: 1.2124
Epoch 1/2.. Steps 310.. Discriminator Loss: 1.0602.. Generator Loss: 0.9281
Epoch 1/2.. Steps 320.. Discriminator Loss: 1.0723.. Generator Loss: 1.1004
Epoch 1/2.. Steps 330.. Discriminator Loss: 1.1862.. Generator Loss: 1.1919
Epoch 1/2.. Steps 340.. Discriminator Loss: 0.9660.. Generator Loss: 1.2665
Epoch 1/2.. Steps 350.. Discriminator Loss: 1.1653.. Generator Loss: 1.3728
Epoch 1/2.. Steps 360.. Discriminator Loss: 1.3368.. Generator Loss: 0.6093
Epoch 1/2.. Steps 370.. Discriminator Loss: 1.4793.. Generator Loss: 0.5075
Epoch 1/2.. Steps 380.. Discriminator Loss: 1.1077.. Generator Loss: 0.8565
Epoch 1/2.. Steps 390.. Discriminator Loss: 0.9848.. Generator Loss: 0.9139
Epoch 1/2.. Steps 400.. Discriminator Loss: 1.2097.. Generator Loss: 0.8202
Epoch 1/2.. Steps 410.. Discriminator Loss: 2.0917.. Generator Loss: 0.2426
Epoch 1/2.. Steps 420.. Discriminator Loss: 0.8979.. Generator Loss: 1.5335
Epoch 1/2.. Steps 430.. Discriminator Loss: 1.1787.. Generator Loss: 1.0838
Epoch 1/2.. Steps 440.. Discriminator Loss: 1.2189.. Generator Loss: 1.1152
Epoch 1/2.. Steps 450.. Discriminator Loss: 0.9227.. Generator Loss: 1.4083
Epoch 1/2.. Steps 460.. Discriminator Loss: 1.3570.. Generator Loss: 1.7448
Epoch 1/2.. Steps 470.. Discriminator Loss: 1.0230.. Generator Loss: 1.2297
Epoch 1/2.. Steps 480.. Discriminator Loss: 1.0124.. Generator Loss: 1.1699
Epoch 1/2.. Steps 490.. Discriminator Loss: 0.9411.. Generator Loss: 1.3124
Epoch 1/2.. Steps 500.. Discriminator Loss: 1.4977.. Generator Loss: 0.4854
Epoch 1/2.. Steps 510.. Discriminator Loss: 1.1666.. Generator Loss: 1.1717
Epoch 1/2.. Steps 520.. Discriminator Loss: 1.1590.. Generator Loss: 0.7593
Epoch 1/2.. Steps 530.. Discriminator Loss: 1.0940.. Generator Loss: 1.1173
Epoch 1/2.. Steps 540.. Discriminator Loss: 1.0957.. Generator Loss: 1.3239
Epoch 1/2.. Steps 550.. Discriminator Loss: 1.0921.. Generator Loss: 1.6793
Epoch 1/2.. Steps 560.. Discriminator Loss: 1.4375.. Generator Loss: 2.1581
Epoch 1/2.. Steps 570.. Discriminator Loss: 1.0778.. Generator Loss: 1.3166
Epoch 1/2.. Steps 580.. Discriminator Loss: 1.4097.. Generator Loss: 0.5848
Epoch 1/2.. Steps 590.. Discriminator Loss: 1.0174.. Generator Loss: 1.7408
Epoch 1/2.. Steps 600.. Discriminator Loss: 1.2783.. Generator Loss: 0.9231
Epoch 1/2.. Steps 610.. Discriminator Loss: 1.0044.. Generator Loss: 1.0216
Epoch 1/2.. Steps 620.. Discriminator Loss: 1.1564.. Generator Loss: 1.4193
Epoch 1/2.. Steps 630.. Discriminator Loss: 1.1569.. Generator Loss: 1.6060
Epoch 1/2.. Steps 640.. Discriminator Loss: 1.1688.. Generator Loss: 1.1146
Epoch 1/2.. Steps 650.. Discriminator Loss: 1.1647.. Generator Loss: 1.5179
Epoch 1/2.. Steps 660.. Discriminator Loss: 1.0606.. Generator Loss: 1.2970
Epoch 1/2.. Steps 670.. Discriminator Loss: 1.3875.. Generator Loss: 1.6299
Epoch 1/2.. Steps 680.. Discriminator Loss: 1.0991.. Generator Loss: 0.9810
Epoch 1/2.. Steps 690.. Discriminator Loss: 1.1265.. Generator Loss: 0.9029
Epoch 1/2.. Steps 700.. Discriminator Loss: 0.9933.. Generator Loss: 1.0268
Epoch 1/2.. Steps 710.. Discriminator Loss: 1.3797.. Generator Loss: 0.6974
Epoch 1/2.. Steps 720.. Discriminator Loss: 0.9854.. Generator Loss: 1.1499
Epoch 1/2.. Steps 730.. Discriminator Loss: 1.1809.. Generator Loss: 0.7138
Epoch 1/2.. Steps 740.. Discriminator Loss: 1.0635.. Generator Loss: 0.9494
Epoch 1/2.. Steps 750.. Discriminator Loss: 0.9340.. Generator Loss: 1.8600
Epoch 1/2.. Steps 760.. Discriminator Loss: 1.3034.. Generator Loss: 2.2559
Epoch 1/2.. Steps 770.. Discriminator Loss: 1.0169.. Generator Loss: 1.1571
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Epoch 2/2.. Steps 1880.. Discriminator Loss: 0.7872.. Generator Loss: 1.8178
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Epoch 2/2.. Steps 2020.. Discriminator Loss: 0.9119.. Generator Loss: 1.3278
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Epoch 2/2.. Steps 2060.. Discriminator Loss: 0.8600.. Generator Loss: 1.1523
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Epoch 2/2.. Steps 2120.. Discriminator Loss: 3.7412.. Generator Loss: 5.7925
Epoch 2/2.. Steps 2130.. Discriminator Loss: 0.8486.. Generator Loss: 1.4623
Epoch 2/2.. Steps 2140.. Discriminator Loss: 1.1447.. Generator Loss: 1.8886
Epoch 2/2.. Steps 2150.. Discriminator Loss: 1.2754.. Generator Loss: 0.6093
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Epoch 2/2.. Steps 2180.. Discriminator Loss: 0.9590.. Generator Loss: 1.0244
Epoch 2/2.. Steps 2190.. Discriminator Loss: 0.6863.. Generator Loss: 1.7993
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Epoch 2/2.. Steps 2210.. Discriminator Loss: 1.0040.. Generator Loss: 1.0126
Epoch 2/2.. Steps 2220.. Discriminator Loss: 1.1659.. Generator Loss: 2.4370
Epoch 2/2.. Steps 2230.. Discriminator Loss: 0.8492.. Generator Loss: 1.6892
Epoch 2/2.. Steps 2240.. Discriminator Loss: 0.7366.. Generator Loss: 1.4377
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Epoch 2/2.. Steps 2260.. Discriminator Loss: 0.7959.. Generator Loss: 1.8342
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Epoch 2/2.. Steps 2310.. Discriminator Loss: 1.0462.. Generator Loss: 1.1181
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Epoch 2/2.. Steps 2410.. Discriminator Loss: 0.8596.. Generator Loss: 1.0884
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Epoch 2/2.. Steps 2430.. Discriminator Loss: 0.7734.. Generator Loss: 1.3117
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Epoch 2/2.. Steps 2470.. Discriminator Loss: 2.1610.. Generator Loss: 0.2402
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Epoch 2/2.. Steps 2490.. Discriminator Loss: 0.7291.. Generator Loss: 1.4315
Epoch 2/2.. Steps 2500.. Discriminator Loss: 1.3178.. Generator Loss: 0.6539
Epoch 2/2.. Steps 2510.. Discriminator Loss: 1.1531.. Generator Loss: 0.9158
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Epoch 2/2.. Steps 2530.. Discriminator Loss: 0.8147.. Generator Loss: 1.2194
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Epoch 2/2.. Steps 2600.. Discriminator Loss: 0.6578.. Generator Loss: 1.7019
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Epoch 2/2.. Steps 2680.. Discriminator Loss: 0.7344.. Generator Loss: 1.8338
Epoch 2/2.. Steps 2690.. Discriminator Loss: 0.5240.. Generator Loss: 2.1385
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Epoch 2/2.. Steps 2710.. Discriminator Loss: 0.8443.. Generator Loss: 1.8814
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Epoch 2/2.. Steps 2740.. Discriminator Loss: 0.7605.. Generator Loss: 1.4856
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Epoch 2/2.. Steps 2760.. Discriminator Loss: 0.9712.. Generator Loss: 1.7358
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Epoch 2/2.. Steps 2800.. Discriminator Loss: 1.3578.. Generator Loss: 0.5488
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Epoch 2/2.. Steps 2850.. Discriminator Loss: 0.8623.. Generator Loss: 1.1864
Epoch 2/2.. Steps 2860.. Discriminator Loss: 0.9053.. Generator Loss: 1.8163
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Epoch 2/2.. Steps 2910.. Discriminator Loss: 0.6641.. Generator Loss: 1.5976
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Epoch 2/2.. Steps 3000.. Discriminator Loss: 1.0348.. Generator Loss: 0.9738
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Epoch 2/2.. Steps 3040.. Discriminator Loss: 1.2692.. Generator Loss: 0.7249
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Epoch 2/2.. Steps 3100.. Discriminator Loss: 2.1980.. Generator Loss: 0.3263
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Epoch 2/2.. Steps 3120.. Discriminator Loss: 0.6877.. Generator Loss: 1.7393
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Epoch 2/2.. Steps 3230.. Discriminator Loss: 2.0573.. Generator Loss: 0.3755
Epoch 2/2.. Steps 3240.. Discriminator Loss: 2.0100.. Generator Loss: 4.3413
Epoch 2/2.. Steps 3250.. Discriminator Loss: 1.0925.. Generator Loss: 0.8241
Epoch 2/2.. Steps 3260.. Discriminator Loss: 0.7530.. Generator Loss: 1.5687
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Epoch 2/2.. Steps 3280.. Discriminator Loss: 1.0551.. Generator Loss: 1.7207
Epoch 2/2.. Steps 3290.. Discriminator Loss: 0.9199.. Generator Loss: 0.9797
Epoch 2/2.. Steps 3300.. Discriminator Loss: 0.6885.. Generator Loss: 1.7403
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Epoch 2/2.. Steps 3320.. Discriminator Loss: 0.5419.. Generator Loss: 2.2402
Epoch 2/2.. Steps 3330.. Discriminator Loss: 2.0003.. Generator Loss: 4.5755
Epoch 2/2.. Steps 3340.. Discriminator Loss: 0.7380.. Generator Loss: 1.5779
Epoch 2/2.. Steps 3350.. Discriminator Loss: 1.3018.. Generator Loss: 0.6203
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Epoch 2/2.. Steps 3370.. Discriminator Loss: 0.9741.. Generator Loss: 1.7911
Epoch 2/2.. Steps 3380.. Discriminator Loss: 0.6948.. Generator Loss: 1.6471
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Epoch 2/2.. Steps 3400.. Discriminator Loss: 1.3876.. Generator Loss: 0.5515
Epoch 2/2.. Steps 3410.. Discriminator Loss: 1.3794.. Generator Loss: 0.6482
Epoch 2/2.. Steps 3420.. Discriminator Loss: 0.7516.. Generator Loss: 1.8352
Epoch 2/2.. Steps 3430.. Discriminator Loss: 0.9356.. Generator Loss: 0.9509
Epoch 2/2.. Steps 3440.. Discriminator Loss: 0.9483.. Generator Loss: 1.0077
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Epoch 2/2.. Steps 3460.. Discriminator Loss: 1.1081.. Generator Loss: 0.7705
Epoch 2/2.. Steps 3470.. Discriminator Loss: 1.2248.. Generator Loss: 0.7278
Epoch 2/2.. Steps 3480.. Discriminator Loss: 0.7097.. Generator Loss: 1.5330
Epoch 2/2.. Steps 3490.. Discriminator Loss: 0.8134.. Generator Loss: 2.0202
Epoch 2/2.. Steps 3500.. Discriminator Loss: 1.5647.. Generator Loss: 0.5167
Epoch 2/2.. Steps 3510.. Discriminator Loss: 0.9238.. Generator Loss: 1.1342
Epoch 2/2.. Steps 3520.. Discriminator Loss: 0.6452.. Generator Loss: 1.8146
Epoch 2/2.. Steps 3530.. Discriminator Loss: 1.6324.. Generator Loss: 0.4098
Epoch 2/2.. Steps 3540.. Discriminator Loss: 0.7310.. Generator Loss: 1.4494
Epoch 2/2.. Steps 3550.. Discriminator Loss: 1.2433.. Generator Loss: 0.6688
Epoch 2/2.. Steps 3560.. Discriminator Loss: 0.8210.. Generator Loss: 1.2482
Epoch 2/2.. Steps 3570.. Discriminator Loss: 1.7897.. Generator Loss: 0.3404
Epoch 2/2.. Steps 3580.. Discriminator Loss: 0.6030.. Generator Loss: 1.8337
Epoch 2/2.. Steps 3590.. Discriminator Loss: 0.6342.. Generator Loss: 1.7851
Epoch 2/2.. Steps 3600.. Discriminator Loss: 0.7720.. Generator Loss: 1.9151
Epoch 2/2.. Steps 3610.. Discriminator Loss: 0.6052.. Generator Loss: 2.0792
Epoch 2/2.. Steps 3620.. Discriminator Loss: 1.1959.. Generator Loss: 2.1978
Epoch 2/2.. Steps 3630.. Discriminator Loss: 0.8283.. Generator Loss: 1.1921
Epoch 2/2.. Steps 3640.. Discriminator Loss: 0.7854.. Generator Loss: 1.1979
Epoch 2/2.. Steps 3650.. Discriminator Loss: 1.3723.. Generator Loss: 0.6122
Epoch 2/2.. Steps 3660.. Discriminator Loss: 0.7648.. Generator Loss: 1.5078
Epoch 2/2.. Steps 3670.. Discriminator Loss: 0.7159.. Generator Loss: 1.4397
Epoch 2/2.. Steps 3680.. Discriminator Loss: 0.8409.. Generator Loss: 1.3080
Epoch 2/2.. Steps 3690.. Discriminator Loss: 0.9640.. Generator Loss: 0.9143
Epoch 2/2.. Steps 3700.. Discriminator Loss: 0.8992.. Generator Loss: 1.1385
Epoch 2/2.. Steps 3710.. Discriminator Loss: 0.8303.. Generator Loss: 2.4424
Epoch 2/2.. Steps 3720.. Discriminator Loss: 1.8400.. Generator Loss: 0.4059
Epoch 2/2.. Steps 3730.. Discriminator Loss: 0.8188.. Generator Loss: 1.1980
Epoch 2/2.. Steps 3740.. Discriminator Loss: 0.9898.. Generator Loss: 0.8932
Epoch 2/2.. Steps 3750.. Discriminator Loss: 0.7131.. Generator Loss: 1.4501

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [15]:
batch_size = 32
z_dim = 100
learning_rate = 0.0008
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1.. Steps 10.. Discriminator Loss: 6.3781.. Generator Loss: 0.0031
Epoch 1/1.. Steps 20.. Discriminator Loss: 1.1140.. Generator Loss: 2.9487
Epoch 1/1.. Steps 30.. Discriminator Loss: 1.2674.. Generator Loss: 14.0596
Epoch 1/1.. Steps 40.. Discriminator Loss: 1.6637.. Generator Loss: 0.3636
Epoch 1/1.. Steps 50.. Discriminator Loss: 1.3747.. Generator Loss: 0.5062
Epoch 1/1.. Steps 60.. Discriminator Loss: 0.6455.. Generator Loss: 2.1854
Epoch 1/1.. Steps 70.. Discriminator Loss: 0.6152.. Generator Loss: 2.4559
Epoch 1/1.. Steps 80.. Discriminator Loss: 0.7221.. Generator Loss: 1.8561
Epoch 1/1.. Steps 90.. Discriminator Loss: 0.9849.. Generator Loss: 1.1864
Epoch 1/1.. Steps 100.. Discriminator Loss: 1.1649.. Generator Loss: 1.5483
Epoch 1/1.. Steps 110.. Discriminator Loss: 1.1337.. Generator Loss: 3.5405
Epoch 1/1.. Steps 120.. Discriminator Loss: 1.5002.. Generator Loss: 3.6242
Epoch 1/1.. Steps 130.. Discriminator Loss: 0.7323.. Generator Loss: 1.5320
Epoch 1/1.. Steps 140.. Discriminator Loss: 0.7185.. Generator Loss: 1.5488
Epoch 1/1.. Steps 150.. Discriminator Loss: 1.2321.. Generator Loss: 0.8912
Epoch 1/1.. Steps 160.. Discriminator Loss: 0.8962.. Generator Loss: 1.8613
Epoch 1/1.. Steps 170.. Discriminator Loss: 1.2423.. Generator Loss: 0.8785
Epoch 1/1.. Steps 180.. Discriminator Loss: 1.0113.. Generator Loss: 0.9615
Epoch 1/1.. Steps 190.. Discriminator Loss: 1.2734.. Generator Loss: 0.6879
Epoch 1/1.. Steps 200.. Discriminator Loss: 1.3486.. Generator Loss: 0.9578
Epoch 1/1.. Steps 210.. Discriminator Loss: 0.8702.. Generator Loss: 1.4743
Epoch 1/1.. Steps 220.. Discriminator Loss: 1.1223.. Generator Loss: 1.5519
Epoch 1/1.. Steps 230.. Discriminator Loss: 1.3016.. Generator Loss: 0.7407
Epoch 1/1.. Steps 240.. Discriminator Loss: 1.5810.. Generator Loss: 0.6941
Epoch 1/1.. Steps 250.. Discriminator Loss: 1.4081.. Generator Loss: 0.6462
Epoch 1/1.. Steps 260.. Discriminator Loss: 0.6455.. Generator Loss: 2.0592
Epoch 1/1.. Steps 270.. Discriminator Loss: 1.0386.. Generator Loss: 1.3890
Epoch 1/1.. Steps 280.. Discriminator Loss: 0.8137.. Generator Loss: 1.7336
Epoch 1/1.. Steps 290.. Discriminator Loss: 0.7494.. Generator Loss: 1.9143
Epoch 1/1.. Steps 300.. Discriminator Loss: 1.0287.. Generator Loss: 1.3739
Epoch 1/1.. Steps 310.. Discriminator Loss: 1.6186.. Generator Loss: 0.4888
Epoch 1/1.. Steps 320.. Discriminator Loss: 1.0424.. Generator Loss: 1.8413
Epoch 1/1.. Steps 330.. Discriminator Loss: 1.1651.. Generator Loss: 1.9346
Epoch 1/1.. Steps 340.. Discriminator Loss: 1.8789.. Generator Loss: 0.4063
Epoch 1/1.. Steps 350.. Discriminator Loss: 1.1295.. Generator Loss: 0.8899
Epoch 1/1.. Steps 360.. Discriminator Loss: 1.5918.. Generator Loss: 0.5067
Epoch 1/1.. Steps 370.. Discriminator Loss: 1.7883.. Generator Loss: 0.3517
Epoch 1/1.. Steps 380.. Discriminator Loss: 1.4031.. Generator Loss: 0.5756
Epoch 1/1.. Steps 390.. Discriminator Loss: 2.0203.. Generator Loss: 0.3783
Epoch 1/1.. Steps 400.. Discriminator Loss: 0.8533.. Generator Loss: 1.3516
Epoch 1/1.. Steps 410.. Discriminator Loss: 1.1230.. Generator Loss: 0.7058
Epoch 1/1.. Steps 420.. Discriminator Loss: 1.1809.. Generator Loss: 1.0937
Epoch 1/1.. Steps 430.. Discriminator Loss: 1.2347.. Generator Loss: 0.6195
Epoch 1/1.. Steps 440.. Discriminator Loss: 1.3571.. Generator Loss: 1.1422
Epoch 1/1.. Steps 450.. Discriminator Loss: 1.2703.. Generator Loss: 1.1360
Epoch 1/1.. Steps 460.. Discriminator Loss: 0.8585.. Generator Loss: 1.5222
Epoch 1/1.. Steps 470.. Discriminator Loss: 1.2047.. Generator Loss: 0.8786
Epoch 1/1.. Steps 480.. Discriminator Loss: 1.0472.. Generator Loss: 1.1276
Epoch 1/1.. Steps 490.. Discriminator Loss: 1.6593.. Generator Loss: 2.4581
Epoch 1/1.. Steps 500.. Discriminator Loss: 1.0665.. Generator Loss: 2.5721
Epoch 1/1.. Steps 510.. Discriminator Loss: 1.2302.. Generator Loss: 0.7591
Epoch 1/1.. Steps 520.. Discriminator Loss: 1.1031.. Generator Loss: 0.8976
Epoch 1/1.. Steps 530.. Discriminator Loss: 1.0229.. Generator Loss: 1.1585
Epoch 1/1.. Steps 540.. Discriminator Loss: 0.8614.. Generator Loss: 1.6764
Epoch 1/1.. Steps 550.. Discriminator Loss: 1.1497.. Generator Loss: 0.9160
Epoch 1/1.. Steps 560.. Discriminator Loss: 0.8195.. Generator Loss: 1.5677
Epoch 1/1.. Steps 570.. Discriminator Loss: 1.1271.. Generator Loss: 0.7255
Epoch 1/1.. Steps 580.. Discriminator Loss: 0.9574.. Generator Loss: 1.0793
Epoch 1/1.. Steps 590.. Discriminator Loss: 1.1176.. Generator Loss: 1.2457
Epoch 1/1.. Steps 600.. Discriminator Loss: 1.1642.. Generator Loss: 0.7475
Epoch 1/1.. Steps 610.. Discriminator Loss: 0.8415.. Generator Loss: 1.3658
Epoch 1/1.. Steps 620.. Discriminator Loss: 0.8322.. Generator Loss: 2.0288
Epoch 1/1.. Steps 630.. Discriminator Loss: 1.0755.. Generator Loss: 1.6963
Epoch 1/1.. Steps 640.. Discriminator Loss: 0.9316.. Generator Loss: 1.0009
Epoch 1/1.. Steps 650.. Discriminator Loss: 0.9253.. Generator Loss: 1.4169
Epoch 1/1.. Steps 660.. Discriminator Loss: 1.1562.. Generator Loss: 1.3876
Epoch 1/1.. Steps 670.. Discriminator Loss: 1.5744.. Generator Loss: 0.4909
Epoch 1/1.. Steps 680.. Discriminator Loss: 1.2773.. Generator Loss: 2.8604
Epoch 1/1.. Steps 690.. Discriminator Loss: 0.9567.. Generator Loss: 1.4290
Epoch 1/1.. Steps 700.. Discriminator Loss: 0.9371.. Generator Loss: 3.0137
Epoch 1/1.. Steps 710.. Discriminator Loss: 0.9168.. Generator Loss: 0.9248
Epoch 1/1.. Steps 720.. Discriminator Loss: 1.4494.. Generator Loss: 3.2759
Epoch 1/1.. Steps 730.. Discriminator Loss: 0.9871.. Generator Loss: 1.3579
Epoch 1/1.. Steps 740.. Discriminator Loss: 1.2283.. Generator Loss: 0.8919
Epoch 1/1.. Steps 750.. Discriminator Loss: 1.2635.. Generator Loss: 1.0300
Epoch 1/1.. Steps 760.. Discriminator Loss: 0.9982.. Generator Loss: 1.4374
Epoch 1/1.. Steps 770.. Discriminator Loss: 1.3801.. Generator Loss: 0.9785
Epoch 1/1.. Steps 780.. Discriminator Loss: 0.9746.. Generator Loss: 1.3946
Epoch 1/1.. Steps 790.. Discriminator Loss: 1.1098.. Generator Loss: 2.4838
Epoch 1/1.. Steps 800.. Discriminator Loss: 0.9652.. Generator Loss: 1.1554
Epoch 1/1.. Steps 810.. Discriminator Loss: 0.8564.. Generator Loss: 1.7931
Epoch 1/1.. Steps 820.. Discriminator Loss: 1.2742.. Generator Loss: 0.7559
Epoch 1/1.. Steps 830.. Discriminator Loss: 0.8257.. Generator Loss: 1.2898
Epoch 1/1.. Steps 840.. Discriminator Loss: 1.1219.. Generator Loss: 1.0718
Epoch 1/1.. Steps 850.. Discriminator Loss: 0.9553.. Generator Loss: 1.2316
Epoch 1/1.. Steps 860.. Discriminator Loss: 1.1135.. Generator Loss: 1.5486
Epoch 1/1.. Steps 870.. Discriminator Loss: 0.8673.. Generator Loss: 1.1104
Epoch 1/1.. Steps 880.. Discriminator Loss: 1.9352.. Generator Loss: 2.8123
Epoch 1/1.. Steps 890.. Discriminator Loss: 1.2135.. Generator Loss: 1.2679
Epoch 1/1.. Steps 900.. Discriminator Loss: 1.1907.. Generator Loss: 1.0844
Epoch 1/1.. Steps 910.. Discriminator Loss: 0.8669.. Generator Loss: 1.5348
Epoch 1/1.. Steps 920.. Discriminator Loss: 1.3628.. Generator Loss: 3.7438
Epoch 1/1.. Steps 930.. Discriminator Loss: 0.9950.. Generator Loss: 1.6786
Epoch 1/1.. Steps 940.. Discriminator Loss: 1.1484.. Generator Loss: 0.7728
Epoch 1/1.. Steps 950.. Discriminator Loss: 1.0271.. Generator Loss: 1.6654
Epoch 1/1.. Steps 960.. Discriminator Loss: 1.5151.. Generator Loss: 0.4811
Epoch 1/1.. Steps 970.. Discriminator Loss: 0.8295.. Generator Loss: 1.1488
Epoch 1/1.. Steps 980.. Discriminator Loss: 1.0969.. Generator Loss: 1.3304
Epoch 1/1.. Steps 990.. Discriminator Loss: 1.0034.. Generator Loss: 1.1835
Epoch 1/1.. Steps 1000.. Discriminator Loss: 1.1733.. Generator Loss: 1.2474
Epoch 1/1.. Steps 1010.. Discriminator Loss: 0.8809.. Generator Loss: 1.4872
Epoch 1/1.. Steps 1020.. Discriminator Loss: 1.2007.. Generator Loss: 0.9525
Epoch 1/1.. Steps 1030.. Discriminator Loss: 1.1745.. Generator Loss: 0.8876
Epoch 1/1.. Steps 1040.. Discriminator Loss: 1.1123.. Generator Loss: 1.2687
Epoch 1/1.. Steps 1050.. Discriminator Loss: 1.4252.. Generator Loss: 0.8065
Epoch 1/1.. Steps 1060.. Discriminator Loss: 1.2134.. Generator Loss: 1.1845
Epoch 1/1.. Steps 1070.. Discriminator Loss: 1.1265.. Generator Loss: 0.8483
Epoch 1/1.. Steps 1080.. Discriminator Loss: 1.2153.. Generator Loss: 0.8747
Epoch 1/1.. Steps 1090.. Discriminator Loss: 1.0601.. Generator Loss: 1.5394
Epoch 1/1.. Steps 1100.. Discriminator Loss: 0.8445.. Generator Loss: 1.6029
Epoch 1/1.. Steps 1110.. Discriminator Loss: 1.0214.. Generator Loss: 0.8929
Epoch 1/1.. Steps 1120.. Discriminator Loss: 1.0783.. Generator Loss: 1.5002
Epoch 1/1.. Steps 1130.. Discriminator Loss: 1.0246.. Generator Loss: 2.9529
Epoch 1/1.. Steps 1140.. Discriminator Loss: 1.6174.. Generator Loss: 1.1975
Epoch 1/1.. Steps 1150.. Discriminator Loss: 1.2797.. Generator Loss: 0.5936
Epoch 1/1.. Steps 1160.. Discriminator Loss: 0.9349.. Generator Loss: 1.2307
Epoch 1/1.. Steps 1170.. Discriminator Loss: 1.0768.. Generator Loss: 1.4578
Epoch 1/1.. Steps 1180.. Discriminator Loss: 0.9653.. Generator Loss: 1.6097
Epoch 1/1.. Steps 1190.. Discriminator Loss: 1.0651.. Generator Loss: 1.2500
Epoch 1/1.. Steps 1200.. Discriminator Loss: 1.7383.. Generator Loss: 0.4664
Epoch 1/1.. Steps 1210.. Discriminator Loss: 1.0609.. Generator Loss: 1.4971
Epoch 1/1.. Steps 1220.. Discriminator Loss: 1.0340.. Generator Loss: 1.1631
Epoch 1/1.. Steps 1230.. Discriminator Loss: 1.2030.. Generator Loss: 1.2648
Epoch 1/1.. Steps 1240.. Discriminator Loss: 0.7745.. Generator Loss: 1.5023
Epoch 1/1.. Steps 1250.. Discriminator Loss: 0.8839.. Generator Loss: 1.1223
Epoch 1/1.. Steps 1260.. Discriminator Loss: 1.1692.. Generator Loss: 0.8121
Epoch 1/1.. Steps 1270.. Discriminator Loss: 0.9392.. Generator Loss: 1.5387
Epoch 1/1.. Steps 1280.. Discriminator Loss: 0.9220.. Generator Loss: 1.3907
Epoch 1/1.. Steps 1290.. Discriminator Loss: 1.3067.. Generator Loss: 0.7799
Epoch 1/1.. Steps 1300.. Discriminator Loss: 1.1973.. Generator Loss: 0.9440
Epoch 1/1.. Steps 1310.. Discriminator Loss: 1.0766.. Generator Loss: 1.0536
Epoch 1/1.. Steps 1320.. Discriminator Loss: 0.9423.. Generator Loss: 1.3369
Epoch 1/1.. Steps 1330.. Discriminator Loss: 1.2877.. Generator Loss: 0.8034
Epoch 1/1.. Steps 1340.. Discriminator Loss: 1.1950.. Generator Loss: 0.7611
Epoch 1/1.. Steps 1350.. Discriminator Loss: 1.1208.. Generator Loss: 2.2861
Epoch 1/1.. Steps 1360.. Discriminator Loss: 0.8719.. Generator Loss: 1.1180
Epoch 1/1.. Steps 1370.. Discriminator Loss: 1.1873.. Generator Loss: 1.1675
Epoch 1/1.. Steps 1380.. Discriminator Loss: 1.0432.. Generator Loss: 0.8842
Epoch 1/1.. Steps 1390.. Discriminator Loss: 0.9938.. Generator Loss: 1.2292
Epoch 1/1.. Steps 1400.. Discriminator Loss: 1.3922.. Generator Loss: 0.6313
Epoch 1/1.. Steps 1410.. Discriminator Loss: 1.0401.. Generator Loss: 1.0285
Epoch 1/1.. Steps 1420.. Discriminator Loss: 1.2203.. Generator Loss: 1.6692
Epoch 1/1.. Steps 1430.. Discriminator Loss: 0.9015.. Generator Loss: 1.2260
Epoch 1/1.. Steps 1440.. Discriminator Loss: 0.9283.. Generator Loss: 1.4237
Epoch 1/1.. Steps 1450.. Discriminator Loss: 1.0053.. Generator Loss: 0.9972
Epoch 1/1.. Steps 1460.. Discriminator Loss: 1.2161.. Generator Loss: 1.7993
Epoch 1/1.. Steps 1470.. Discriminator Loss: 0.9069.. Generator Loss: 1.9315
Epoch 1/1.. Steps 1480.. Discriminator Loss: 0.9479.. Generator Loss: 1.1961
Epoch 1/1.. Steps 1490.. Discriminator Loss: 0.9722.. Generator Loss: 0.9797
Epoch 1/1.. Steps 1500.. Discriminator Loss: 1.0298.. Generator Loss: 1.2149
Epoch 1/1.. Steps 1510.. Discriminator Loss: 0.9731.. Generator Loss: 1.5596
Epoch 1/1.. Steps 1520.. Discriminator Loss: 1.8398.. Generator Loss: 0.3350
Epoch 1/1.. Steps 1530.. Discriminator Loss: 1.0491.. Generator Loss: 1.7607
Epoch 1/1.. Steps 1540.. Discriminator Loss: 0.8653.. Generator Loss: 1.2340
Epoch 1/1.. Steps 1550.. Discriminator Loss: 0.8323.. Generator Loss: 1.2121
Epoch 1/1.. Steps 1560.. Discriminator Loss: 0.8627.. Generator Loss: 2.1785
Epoch 1/1.. Steps 1570.. Discriminator Loss: 1.3218.. Generator Loss: 0.6357
Epoch 1/1.. Steps 1580.. Discriminator Loss: 1.1437.. Generator Loss: 0.8191
Epoch 1/1.. Steps 1590.. Discriminator Loss: 1.0630.. Generator Loss: 1.0004
Epoch 1/1.. Steps 1600.. Discriminator Loss: 1.1827.. Generator Loss: 0.7328
Epoch 1/1.. Steps 1610.. Discriminator Loss: 1.0102.. Generator Loss: 1.7030
Epoch 1/1.. Steps 1620.. Discriminator Loss: 1.0871.. Generator Loss: 0.8424
Epoch 1/1.. Steps 1630.. Discriminator Loss: 1.0889.. Generator Loss: 1.4875
Epoch 1/1.. Steps 1640.. Discriminator Loss: 1.2981.. Generator Loss: 0.6218
Epoch 1/1.. Steps 1650.. Discriminator Loss: 1.3631.. Generator Loss: 0.6589
Epoch 1/1.. Steps 1660.. Discriminator Loss: 1.0866.. Generator Loss: 1.8602
Epoch 1/1.. Steps 1670.. Discriminator Loss: 1.0882.. Generator Loss: 1.6115
Epoch 1/1.. Steps 1680.. Discriminator Loss: 1.2242.. Generator Loss: 0.6847
Epoch 1/1.. Steps 1690.. Discriminator Loss: 1.2909.. Generator Loss: 1.6772
Epoch 1/1.. Steps 1700.. Discriminator Loss: 1.0484.. Generator Loss: 1.3432
Epoch 1/1.. Steps 1710.. Discriminator Loss: 1.0318.. Generator Loss: 1.6459
Epoch 1/1.. Steps 1720.. Discriminator Loss: 0.9752.. Generator Loss: 1.4314
Epoch 1/1.. Steps 1730.. Discriminator Loss: 1.0414.. Generator Loss: 0.9969
Epoch 1/1.. Steps 1740.. Discriminator Loss: 1.1398.. Generator Loss: 1.0067
Epoch 1/1.. Steps 1750.. Discriminator Loss: 0.9810.. Generator Loss: 1.0541
Epoch 1/1.. Steps 1760.. Discriminator Loss: 1.0858.. Generator Loss: 0.9645
Epoch 1/1.. Steps 1770.. Discriminator Loss: 1.0599.. Generator Loss: 1.1312
Epoch 1/1.. Steps 1780.. Discriminator Loss: 1.0287.. Generator Loss: 1.2915
Epoch 1/1.. Steps 1790.. Discriminator Loss: 0.8868.. Generator Loss: 1.2123
Epoch 1/1.. Steps 1800.. Discriminator Loss: 1.0230.. Generator Loss: 1.8289
Epoch 1/1.. Steps 1810.. Discriminator Loss: 0.7657.. Generator Loss: 1.6180
Epoch 1/1.. Steps 1820.. Discriminator Loss: 0.8411.. Generator Loss: 1.3076
Epoch 1/1.. Steps 1830.. Discriminator Loss: 1.4005.. Generator Loss: 0.6161
Epoch 1/1.. Steps 1840.. Discriminator Loss: 1.0030.. Generator Loss: 1.1001
Epoch 1/1.. Steps 1850.. Discriminator Loss: 0.9903.. Generator Loss: 1.1680
Epoch 1/1.. Steps 1860.. Discriminator Loss: 0.9946.. Generator Loss: 1.2959
Epoch 1/1.. Steps 1870.. Discriminator Loss: 0.8897.. Generator Loss: 1.1456
Epoch 1/1.. Steps 1880.. Discriminator Loss: 0.9241.. Generator Loss: 1.1519
Epoch 1/1.. Steps 1890.. Discriminator Loss: 0.9784.. Generator Loss: 1.4930
Epoch 1/1.. Steps 1900.. Discriminator Loss: 1.1171.. Generator Loss: 0.9579
Epoch 1/1.. Steps 1910.. Discriminator Loss: 1.2412.. Generator Loss: 0.7413
Epoch 1/1.. Steps 1920.. Discriminator Loss: 1.0623.. Generator Loss: 0.8126
Epoch 1/1.. Steps 1930.. Discriminator Loss: 1.0151.. Generator Loss: 1.1785
Epoch 1/1.. Steps 1940.. Discriminator Loss: 1.0972.. Generator Loss: 0.8829
Epoch 1/1.. Steps 1950.. Discriminator Loss: 1.0595.. Generator Loss: 0.9061
Epoch 1/1.. Steps 1960.. Discriminator Loss: 1.0822.. Generator Loss: 2.0451
Epoch 1/1.. Steps 1970.. Discriminator Loss: 1.0276.. Generator Loss: 1.0481
Epoch 1/1.. Steps 1980.. Discriminator Loss: 1.0473.. Generator Loss: 0.8729
Epoch 1/1.. Steps 1990.. Discriminator Loss: 0.8909.. Generator Loss: 1.2573
Epoch 1/1.. Steps 2000.. Discriminator Loss: 1.0075.. Generator Loss: 1.4413
Epoch 1/1.. Steps 2010.. Discriminator Loss: 1.1287.. Generator Loss: 1.5680
Epoch 1/1.. Steps 2020.. Discriminator Loss: 1.3356.. Generator Loss: 0.5947
Epoch 1/1.. Steps 2030.. Discriminator Loss: 0.9144.. Generator Loss: 1.5251
Epoch 1/1.. Steps 2040.. Discriminator Loss: 0.9179.. Generator Loss: 1.0830
Epoch 1/1.. Steps 2050.. Discriminator Loss: 1.0986.. Generator Loss: 0.9843
Epoch 1/1.. Steps 2060.. Discriminator Loss: 0.9211.. Generator Loss: 1.3089
Epoch 1/1.. Steps 2070.. Discriminator Loss: 1.2544.. Generator Loss: 1.1085
Epoch 1/1.. Steps 2080.. Discriminator Loss: 0.9598.. Generator Loss: 1.4611
Epoch 1/1.. Steps 2090.. Discriminator Loss: 0.8511.. Generator Loss: 1.3674
Epoch 1/1.. Steps 2100.. Discriminator Loss: 1.1446.. Generator Loss: 0.8851
Epoch 1/1.. Steps 2110.. Discriminator Loss: 0.8984.. Generator Loss: 2.0325
Epoch 1/1.. Steps 2120.. Discriminator Loss: 0.9954.. Generator Loss: 2.0774
Epoch 1/1.. Steps 2130.. Discriminator Loss: 0.9747.. Generator Loss: 1.0307
Epoch 1/1.. Steps 2140.. Discriminator Loss: 0.9664.. Generator Loss: 1.0208
Epoch 1/1.. Steps 2150.. Discriminator Loss: 1.0552.. Generator Loss: 1.0719
Epoch 1/1.. Steps 2160.. Discriminator Loss: 0.9058.. Generator Loss: 1.8262
Epoch 1/1.. Steps 2170.. Discriminator Loss: 1.1095.. Generator Loss: 0.8401
Epoch 1/1.. Steps 2180.. Discriminator Loss: 0.9508.. Generator Loss: 1.8729
Epoch 1/1.. Steps 2190.. Discriminator Loss: 1.0928.. Generator Loss: 1.2868
Epoch 1/1.. Steps 2200.. Discriminator Loss: 0.9575.. Generator Loss: 1.0432
Epoch 1/1.. Steps 2210.. Discriminator Loss: 0.9733.. Generator Loss: 1.1114
Epoch 1/1.. Steps 2220.. Discriminator Loss: 1.0396.. Generator Loss: 1.0051
Epoch 1/1.. Steps 2230.. Discriminator Loss: 1.1016.. Generator Loss: 0.7812
Epoch 1/1.. Steps 2240.. Discriminator Loss: 1.1454.. Generator Loss: 0.8552
Epoch 1/1.. Steps 2250.. Discriminator Loss: 1.0412.. Generator Loss: 1.0624
Epoch 1/1.. Steps 2260.. Discriminator Loss: 1.0462.. Generator Loss: 1.2974
Epoch 1/1.. Steps 2270.. Discriminator Loss: 1.2166.. Generator Loss: 0.7018
Epoch 1/1.. Steps 2280.. Discriminator Loss: 1.0205.. Generator Loss: 1.0791
Epoch 1/1.. Steps 2290.. Discriminator Loss: 0.9001.. Generator Loss: 2.0901
Epoch 1/1.. Steps 2300.. Discriminator Loss: 1.2208.. Generator Loss: 1.9812
Epoch 1/1.. Steps 2310.. Discriminator Loss: 1.0021.. Generator Loss: 1.6259
Epoch 1/1.. Steps 2320.. Discriminator Loss: 1.2009.. Generator Loss: 3.0873
Epoch 1/1.. Steps 2330.. Discriminator Loss: 1.2219.. Generator Loss: 0.7175
Epoch 1/1.. Steps 2340.. Discriminator Loss: 1.3014.. Generator Loss: 0.6028
Epoch 1/1.. Steps 2350.. Discriminator Loss: 1.1099.. Generator Loss: 1.2615
Epoch 1/1.. Steps 2360.. Discriminator Loss: 1.1126.. Generator Loss: 0.8223
Epoch 1/1.. Steps 2370.. Discriminator Loss: 1.0593.. Generator Loss: 1.5249
Epoch 1/1.. Steps 2380.. Discriminator Loss: 0.9379.. Generator Loss: 1.1553
Epoch 1/1.. Steps 2390.. Discriminator Loss: 0.9857.. Generator Loss: 2.0308
Epoch 1/1.. Steps 2400.. Discriminator Loss: 0.9305.. Generator Loss: 1.0790
Epoch 1/1.. Steps 2410.. Discriminator Loss: 1.0292.. Generator Loss: 1.1093
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Epoch 1/1.. Steps 5110.. Discriminator Loss: 1.5128.. Generator Loss: 0.4427
Epoch 1/1.. Steps 5120.. Discriminator Loss: 1.1648.. Generator Loss: 0.8390
Epoch 1/1.. Steps 5130.. Discriminator Loss: 1.2351.. Generator Loss: 0.6966
Epoch 1/1.. Steps 5140.. Discriminator Loss: 1.2049.. Generator Loss: 0.9208
Epoch 1/1.. Steps 5150.. Discriminator Loss: 1.2677.. Generator Loss: 0.7035
Epoch 1/1.. Steps 5160.. Discriminator Loss: 1.2665.. Generator Loss: 0.6423
Epoch 1/1.. Steps 5170.. Discriminator Loss: 1.3030.. Generator Loss: 0.6560
Epoch 1/1.. Steps 5180.. Discriminator Loss: 1.0025.. Generator Loss: 1.0131
Epoch 1/1.. Steps 5190.. Discriminator Loss: 1.2615.. Generator Loss: 0.6153
Epoch 1/1.. Steps 5200.. Discriminator Loss: 1.2620.. Generator Loss: 0.8545
Epoch 1/1.. Steps 5210.. Discriminator Loss: 1.1072.. Generator Loss: 1.0549
Epoch 1/1.. Steps 5220.. Discriminator Loss: 1.1035.. Generator Loss: 0.9183
Epoch 1/1.. Steps 5230.. Discriminator Loss: 1.5005.. Generator Loss: 0.4740
Epoch 1/1.. Steps 5240.. Discriminator Loss: 1.1324.. Generator Loss: 1.6786
Epoch 1/1.. Steps 5250.. Discriminator Loss: 1.4095.. Generator Loss: 0.5451
Epoch 1/1.. Steps 5260.. Discriminator Loss: 1.3241.. Generator Loss: 0.5275
Epoch 1/1.. Steps 5270.. Discriminator Loss: 1.1705.. Generator Loss: 0.7318
Epoch 1/1.. Steps 5280.. Discriminator Loss: 1.1104.. Generator Loss: 0.8257
Epoch 1/1.. Steps 5290.. Discriminator Loss: 1.3798.. Generator Loss: 0.6651
Epoch 1/1.. Steps 5300.. Discriminator Loss: 1.1388.. Generator Loss: 0.7249
Epoch 1/1.. Steps 5310.. Discriminator Loss: 1.3358.. Generator Loss: 0.7796
Epoch 1/1.. Steps 5320.. Discriminator Loss: 1.2247.. Generator Loss: 0.8365
Epoch 1/1.. Steps 5330.. Discriminator Loss: 1.1458.. Generator Loss: 1.0896
Epoch 1/1.. Steps 5340.. Discriminator Loss: 1.2501.. Generator Loss: 0.6320
Epoch 1/1.. Steps 5350.. Discriminator Loss: 1.0119.. Generator Loss: 1.1604
Epoch 1/1.. Steps 5360.. Discriminator Loss: 1.0772.. Generator Loss: 1.1937
Epoch 1/1.. Steps 5370.. Discriminator Loss: 1.1136.. Generator Loss: 0.8488
Epoch 1/1.. Steps 5380.. Discriminator Loss: 1.0913.. Generator Loss: 0.8904
Epoch 1/1.. Steps 5390.. Discriminator Loss: 1.2185.. Generator Loss: 0.7938
Epoch 1/1.. Steps 5400.. Discriminator Loss: 1.1364.. Generator Loss: 0.9043
Epoch 1/1.. Steps 5410.. Discriminator Loss: 1.4166.. Generator Loss: 0.5346
Epoch 1/1.. Steps 5420.. Discriminator Loss: 1.0014.. Generator Loss: 1.0848
Epoch 1/1.. Steps 5430.. Discriminator Loss: 1.2039.. Generator Loss: 0.8827
Epoch 1/1.. Steps 5440.. Discriminator Loss: 1.0776.. Generator Loss: 1.2364
Epoch 1/1.. Steps 5450.. Discriminator Loss: 1.0504.. Generator Loss: 1.2149
Epoch 1/1.. Steps 5460.. Discriminator Loss: 1.5024.. Generator Loss: 0.4571
Epoch 1/1.. Steps 5470.. Discriminator Loss: 1.1681.. Generator Loss: 0.8074
Epoch 1/1.. Steps 5480.. Discriminator Loss: 1.4515.. Generator Loss: 0.4822
Epoch 1/1.. Steps 5490.. Discriminator Loss: 1.2473.. Generator Loss: 0.7610
Epoch 1/1.. Steps 5500.. Discriminator Loss: 1.1486.. Generator Loss: 0.7355
Epoch 1/1.. Steps 5510.. Discriminator Loss: 1.0947.. Generator Loss: 0.9286
Epoch 1/1.. Steps 5520.. Discriminator Loss: 1.3010.. Generator Loss: 0.6654
Epoch 1/1.. Steps 5530.. Discriminator Loss: 1.5414.. Generator Loss: 0.4513
Epoch 1/1.. Steps 5540.. Discriminator Loss: 1.0740.. Generator Loss: 0.9198
Epoch 1/1.. Steps 5550.. Discriminator Loss: 1.3859.. Generator Loss: 0.5769
Epoch 1/1.. Steps 5560.. Discriminator Loss: 1.0314.. Generator Loss: 1.0707
Epoch 1/1.. Steps 5570.. Discriminator Loss: 0.9452.. Generator Loss: 1.2609
Epoch 1/1.. Steps 5580.. Discriminator Loss: 1.2148.. Generator Loss: 0.6288
Epoch 1/1.. Steps 5590.. Discriminator Loss: 1.1922.. Generator Loss: 1.5327
Epoch 1/1.. Steps 5600.. Discriminator Loss: 1.2429.. Generator Loss: 0.6522
Epoch 1/1.. Steps 5610.. Discriminator Loss: 1.1850.. Generator Loss: 0.8259
Epoch 1/1.. Steps 5620.. Discriminator Loss: 1.2668.. Generator Loss: 0.6398
Epoch 1/1.. Steps 5630.. Discriminator Loss: 1.2057.. Generator Loss: 0.6521
Epoch 1/1.. Steps 5640.. Discriminator Loss: 1.2519.. Generator Loss: 0.7644
Epoch 1/1.. Steps 5650.. Discriminator Loss: 1.0395.. Generator Loss: 1.3132
Epoch 1/1.. Steps 5660.. Discriminator Loss: 1.3282.. Generator Loss: 0.5937
Epoch 1/1.. Steps 5670.. Discriminator Loss: 1.0503.. Generator Loss: 1.5243
Epoch 1/1.. Steps 5680.. Discriminator Loss: 1.2316.. Generator Loss: 0.6923
Epoch 1/1.. Steps 5690.. Discriminator Loss: 1.4693.. Generator Loss: 0.5651
Epoch 1/1.. Steps 5700.. Discriminator Loss: 1.0453.. Generator Loss: 1.0751
Epoch 1/1.. Steps 5710.. Discriminator Loss: 1.1777.. Generator Loss: 0.7934
Epoch 1/1.. Steps 5720.. Discriminator Loss: 1.1264.. Generator Loss: 1.2444
Epoch 1/1.. Steps 5730.. Discriminator Loss: 1.8499.. Generator Loss: 0.3151
Epoch 1/1.. Steps 5740.. Discriminator Loss: 1.0779.. Generator Loss: 1.0608
Epoch 1/1.. Steps 5750.. Discriminator Loss: 1.1500.. Generator Loss: 0.9671
Epoch 1/1.. Steps 5760.. Discriminator Loss: 1.1647.. Generator Loss: 0.8173
Epoch 1/1.. Steps 5770.. Discriminator Loss: 1.5005.. Generator Loss: 0.4861
Epoch 1/1.. Steps 5780.. Discriminator Loss: 1.0566.. Generator Loss: 1.2539
Epoch 1/1.. Steps 5790.. Discriminator Loss: 1.1853.. Generator Loss: 0.7391
Epoch 1/1.. Steps 5800.. Discriminator Loss: 1.3127.. Generator Loss: 0.6060
Epoch 1/1.. Steps 5810.. Discriminator Loss: 1.1444.. Generator Loss: 1.0804
Epoch 1/1.. Steps 5820.. Discriminator Loss: 1.3243.. Generator Loss: 0.5503
Epoch 1/1.. Steps 5830.. Discriminator Loss: 1.5004.. Generator Loss: 0.4594
Epoch 1/1.. Steps 5840.. Discriminator Loss: 1.0239.. Generator Loss: 1.1465
Epoch 1/1.. Steps 5850.. Discriminator Loss: 1.2695.. Generator Loss: 0.7437
Epoch 1/1.. Steps 5860.. Discriminator Loss: 1.2109.. Generator Loss: 0.7828
Epoch 1/1.. Steps 5870.. Discriminator Loss: 1.2540.. Generator Loss: 0.6952
Epoch 1/1.. Steps 5880.. Discriminator Loss: 1.2433.. Generator Loss: 0.7144
Epoch 1/1.. Steps 5890.. Discriminator Loss: 1.1315.. Generator Loss: 0.7133
Epoch 1/1.. Steps 5900.. Discriminator Loss: 1.0539.. Generator Loss: 1.2614
Epoch 1/1.. Steps 5910.. Discriminator Loss: 0.9775.. Generator Loss: 1.1264
Epoch 1/1.. Steps 5920.. Discriminator Loss: 1.0420.. Generator Loss: 1.0957
Epoch 1/1.. Steps 5930.. Discriminator Loss: 1.2757.. Generator Loss: 0.8396
Epoch 1/1.. Steps 5940.. Discriminator Loss: 0.9725.. Generator Loss: 0.9828
Epoch 1/1.. Steps 5950.. Discriminator Loss: 1.1798.. Generator Loss: 0.8867
Epoch 1/1.. Steps 5960.. Discriminator Loss: 1.2000.. Generator Loss: 0.7319
Epoch 1/1.. Steps 5970.. Discriminator Loss: 1.5470.. Generator Loss: 0.4603
Epoch 1/1.. Steps 5980.. Discriminator Loss: 1.1086.. Generator Loss: 0.8811
Epoch 1/1.. Steps 5990.. Discriminator Loss: 1.1101.. Generator Loss: 1.2658
Epoch 1/1.. Steps 6000.. Discriminator Loss: 1.2133.. Generator Loss: 1.2586
Epoch 1/1.. Steps 6010.. Discriminator Loss: 1.1404.. Generator Loss: 0.7946
Epoch 1/1.. Steps 6020.. Discriminator Loss: 1.1259.. Generator Loss: 1.1345
Epoch 1/1.. Steps 6030.. Discriminator Loss: 0.9872.. Generator Loss: 1.1218
Epoch 1/1.. Steps 6040.. Discriminator Loss: 1.2638.. Generator Loss: 0.9040
Epoch 1/1.. Steps 6050.. Discriminator Loss: 1.1207.. Generator Loss: 0.9271
Epoch 1/1.. Steps 6060.. Discriminator Loss: 1.0622.. Generator Loss: 0.9358
Epoch 1/1.. Steps 6070.. Discriminator Loss: 1.1205.. Generator Loss: 0.8106
Epoch 1/1.. Steps 6080.. Discriminator Loss: 1.2347.. Generator Loss: 0.7301
Epoch 1/1.. Steps 6090.. Discriminator Loss: 1.2086.. Generator Loss: 0.8359
Epoch 1/1.. Steps 6100.. Discriminator Loss: 1.1357.. Generator Loss: 1.0215
Epoch 1/1.. Steps 6110.. Discriminator Loss: 1.1490.. Generator Loss: 0.7988
Epoch 1/1.. Steps 6120.. Discriminator Loss: 1.1027.. Generator Loss: 1.4385
Epoch 1/1.. Steps 6130.. Discriminator Loss: 1.0095.. Generator Loss: 1.1628
Epoch 1/1.. Steps 6140.. Discriminator Loss: 0.9639.. Generator Loss: 1.1792
Epoch 1/1.. Steps 6150.. Discriminator Loss: 1.5607.. Generator Loss: 0.4134
Epoch 1/1.. Steps 6160.. Discriminator Loss: 1.1430.. Generator Loss: 0.8536
Epoch 1/1.. Steps 6170.. Discriminator Loss: 1.3569.. Generator Loss: 0.6200
Epoch 1/1.. Steps 6180.. Discriminator Loss: 1.0409.. Generator Loss: 0.8758
Epoch 1/1.. Steps 6190.. Discriminator Loss: 1.1021.. Generator Loss: 0.8933
Epoch 1/1.. Steps 6200.. Discriminator Loss: 1.1543.. Generator Loss: 0.8532
Epoch 1/1.. Steps 6210.. Discriminator Loss: 1.0873.. Generator Loss: 0.9211
Epoch 1/1.. Steps 6220.. Discriminator Loss: 1.2383.. Generator Loss: 0.6712
Epoch 1/1.. Steps 6230.. Discriminator Loss: 1.3360.. Generator Loss: 0.5399
Epoch 1/1.. Steps 6240.. Discriminator Loss: 1.0457.. Generator Loss: 1.3027
Epoch 1/1.. Steps 6250.. Discriminator Loss: 1.1483.. Generator Loss: 1.1684
Epoch 1/1.. Steps 6260.. Discriminator Loss: 1.5069.. Generator Loss: 0.4416
Epoch 1/1.. Steps 6270.. Discriminator Loss: 1.0548.. Generator Loss: 0.9594
Epoch 1/1.. Steps 6280.. Discriminator Loss: 1.2101.. Generator Loss: 1.0989
Epoch 1/1.. Steps 6290.. Discriminator Loss: 1.1200.. Generator Loss: 0.9276
Epoch 1/1.. Steps 6300.. Discriminator Loss: 1.1326.. Generator Loss: 1.0490
Epoch 1/1.. Steps 6310.. Discriminator Loss: 1.0812.. Generator Loss: 1.4692
Epoch 1/1.. Steps 6320.. Discriminator Loss: 1.0690.. Generator Loss: 0.9149
Epoch 1/1.. Steps 6330.. Discriminator Loss: 1.2786.. Generator Loss: 0.6995

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.